Build a Chatbot for Your Data

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Estimated time needed: 60 min

Introduction

In this project, you will create a chatbot for your own pdf file using Flask, a popular web framework, and LangChain, another popular framework for working with large language models (LLMs). The chatbot you develop will not just interact with users through text but also comprehend and answer questions related to the content of a specific document.

Click on the demo link below to try the final application that you will create!

Try the demo app

At the end of this project, you will gain a deeper understanding of chatbots, web application development using Flask and Python, and the use of LangChain framework in interpreting and responding to a wide array of user inputs. And most important, you would have built a comprehensive and impressive chatbot application!

A person searches for a document in a massive stack of papers.

Before you get started, let's look at the key elements you'll be using to build your application.

Chatbots

Chatbots are software applications designed to engage in human-like conversation. They can respond to text inputs from users and are widely used in various domains, including customer service, eCommerce, and education. In this project, you will build a chatbot capable of not only engaging users in a general conversation but also answering queries based on a particular document.

LangChain

LangChain is a versatile tool for building AI-driven language applications. It provides various functionalities such as text retrieval, summarization, translation, and many more, by leveraging pretrained language models. In this project, you will be integrating LangChain into your chatbot, empowering it to understand and respond to diverse user inputs effectively.

Flask

Flask is a lightweight and flexible web framework for Python, known for its simplicity and speed. A web framework is a software framework designed to support the development of web applications, including the creation of web servers, and management of HTTP requests and responses.

You will use Flask to create the server side or backend of your chatbot. This involves handling incoming messages from users, processing these messages, and sending appropriate responses back to the user.

Routes in Flask

Routes are an essential part of web development. When your application receives a request from a client (typically a web browser), it needs to know how to handle that request. This is where routing comes in.

In Flask, routes are created using the @app.route decorator to bind a function to a URL route. When a user visits that URL, the associated function is executed. In your chatbot project, you will use routes to handle the POST requests carrying user messages and to process document data.

HTML - CSS - JavaScript

You are provided with a ready-to-use chatbot front-end, built with HTML, CSS, and JavaScript. HTML structures web content, CSS styles it and JavaScript adds interactivity. These technologies create a visually appealing and interactive chatbot interface.

Here is an snapshot of the interface:

Chatbot Interface

Learning objectives

At the end of this project, you will be able to:

  • Explain the basics of Langchain and AI applications
  • Set up a development environment for building an assistant using Python Flask
  • Implement PDF upload functionality to allow the assistant to comprehend file input from users
  • Integrate the assistant with open source models to give it a high level of intelligence and the ability to understand and respond to user requests
  • (Optional) Deploy the PDF assistant to a web server for use by a wider audience

Prerequisites

Knowledge of the basics of HTML/CSS, JavaScript, and Python is nice to have but not essential. Each step of the process and code will have a comprehensive explanation in this lab.

With the background in mind, let's get started on your project!